针对户外监控系统需要利用图像画面进行天气状态识别的问题, 提出了一种新的词袋模型, 以及SVM和随机森林相结合的分类方法, 对晴天与阴天两类天气状态进行识别. 词袋模型利用SIFT特征, 通过聚类构建词典,并用最小二乘法求解最佳图像的词典结构参数, 最终根据金字塔匹配得到多尺度图像词袋模型特征. 分类器的构造采用支持向量机(SVM)作为一级分类器, 对小置信样本进行粗分类, 之后, 再利用随机森林构造作为二级分类器进行判别. 通过对两类天气图像集的10000张图像进行测试, 其识别准确率验证了方法的有效性.%Considering the outdoor monitoring system using the image to identify weather conditions, a novel classification method with bag of words model is proposed which combines SVM with random forest to identify the sunny or cloudy days. The bag of words model uses the SIFT feature to construct the dictionary by clustering, and uses the least squares method to solve the dictionary structure parameters of the optimal image. Finally, the multi-scale image bag of words model feature is obtained according to the pyramid matching. The construction of the classifier uses the support vector machine (SVM) as the first level classifier to classify the small confidence samples, and then uses the random forest as the second level classifier to judge. Through the test of the 10 000 images of two categories of weather images, the recognition accuracy verifies the effectiveness of the method.
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